What challenges are associated with trying to pursue precision medicine?

There are multiple challenges. We’ll start with the clinician. So, when the physician comes in to see the patient, there’s so much data that’s out there, and depending on the environment the physician is seeing the patient in also dictates some of this. A physician [who] has to know every single tumor type [is not] going to be able to keep up with the massive data and the studies and everything that’s coming out for every single tumor type. So, keeping up with the data, integrating it, being able to actually teach the patient about it, and implement[ing] it is one of the great challenges that is out there.

Other challenges are who should we be testing with this, what test should we be doing, and keeping the physician up-to-date with that. [On] the patient's end of things, the willingness to do some of the precision techniques, because they do take time, and also their expectations may be somewhat unrealistic. Things that you have to worry about is that the patient says, "Oh great, I have this mutation, I should be responding to X therapy,” or perhaps, “My tumor proportion score of PD-L1 is 80, I should have a great response to one of the checkpoint inhibitors,” and in fact they don’t.

Studies have shown, with most of these agents, response rates are somewhere in about 50 percentile, maybe 54 percentile. A couple actually go up into the 60 to 70 percentile. But, not everybody who should respond is responding, so the expectations can be unrealistic on both the clinicians and the patient standpoint.

As far as testing, that’s a whole other ball of wax. You know, we start out with how do we identify the patients that should be getting testing, and how do we identify, of those patients, who should be getting treatment and who shouldn’t. One of the early examples of this is the Oncotype Dx, which is used in breast cancer to determine patients who have undergone surgery with estrogen-receptor positivity who should be getting adjuvant chemotherapy. Well, that’s only 1 example. How do we pick out other patients who should be being treated and who shouldn’t be being treated?

Another example would be: how do we pick out who is going to respond. What test should we be doing? Should they be immunohistochemistry? Should they be fluorescence in situ hybridization type testing? Should it be something along the lines of next-generation sequencing? Nobody really knows the answer to that, and there are standardization issues, other issues that occur are: what toxicity tests should we be doing?

Other things could include who should be being tested for germline mutations. We usually determine that if a patient comes in and has many of their family members affected by cancer, but those are the only patients that, right now, we’re recommending have germline testing. Are there are other families that we are missing? Other germline mutations we really don’t know about yet?

So, I think overall, it’s a very complicated situation, and really determining which patients should get what tests and as to what patients should get what tests is complicated.

Now, when we get to treatment, what are the actual treatments we should be having? Some of it has been well worked out. A lot of it hasn’t been. The other thing is, we need to have well-developed molecular marker-driven clinical studies for patients to be enrolled in.

The other thing are the master protocols and getting patients to participate in that, and then what about those that are nonresponders. What do we do about them? How do we figure out what the heck is going on with them? So, overall, it’s a very complicated and complex problem.